A novel chemical composition estimation model for cement raw material blending process

doi:10.1016/j.cjche.2018.12.024

中国化学工程学报 ›› 2019, Vol. 27 ›› Issue (11): 2734-2741.DOI: 10.1016/j.cjche.2018.12.024

• Process Systems Engineering and Process Safety • 上一篇下一篇

A novel chemical composition estimation model for cement raw material blending process

Yaoyao Bao, Yuanming Zhu, Weimin Zhong, Feng Qian

Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

收稿日期:2020-11-04 出版日期:2019-11-28 发布日期:2020-01-19
通讯作者: Weimin Zhong, Feng Qian
基金资助:
Supported by the National Key R&D Program of China (2016YFB0303401), the National Natural Science Foundation of China (61333010, 61503138).

A novel chemical composition estimation model for cement raw material blending process

Yaoyao Bao, Yuanming Zhu, Weimin Zhong, Feng Qian

Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2020-11-04 Online:2019-11-28 Published:2020-01-19
Contact: Weimin Zhong, Feng Qian
Supported by:
Supported by the National Key R&D Program of China (2016YFB0303401), the National Natural Science Foundation of China (61333010, 61503138).

摘要/Abstract

摘要： Raw material blending process is an essential part of the cement production process. The main purpose of the process is to guarantee a certain oxide composition for the raw meal at the outlet of the mill by regulating the four raw materials. But the chemical compositions of raw materials vary from time to time, resulting in difficulties to control the oxide compositions to a predefined value. Therefore, a novel algorithm to estimate the chemical compositions of the raw materials is developed. The paper mainly consists of two parts. In model construction part, a novel constrained least square model is proposed to overcome the deviation introduced by long-term drift of the material components, and the model parameters are estimated with an online strategy. And in validation part, the approach is implemented to two examples including datasets from simulation model and the actual industrial process. The final results show the effectiveness of the proposed method.

关键词: Raw material blending process, Chemical component estimation, Modulus prediction, System identification

Abstract: Raw material blending process is an essential part of the cement production process. The main purpose of the process is to guarantee a certain oxide composition for the raw meal at the outlet of the mill by regulating the four raw materials. But the chemical compositions of raw materials vary from time to time, resulting in difficulties to control the oxide compositions to a predefined value. Therefore, a novel algorithm to estimate the chemical compositions of the raw materials is developed. The paper mainly consists of two parts. In model construction part, a novel constrained least square model is proposed to overcome the deviation introduced by long-term drift of the material components, and the model parameters are estimated with an online strategy. And in validation part, the approach is implemented to two examples including datasets from simulation model and the actual industrial process. The final results show the effectiveness of the proposed method.

Key words: Raw material blending process, Chemical component estimation, Modulus prediction, System identification

Yaoyao Bao, Yuanming Zhu, Weimin Zhong, Feng Qian. A novel chemical composition estimation model for cement raw material blending process[J]. 中国化学工程学报, 2019, 27(11): 2734-2741.

Yaoyao Bao, Yuanming Zhu, Weimin Zhong, Feng Qian. A novel chemical composition estimation model for cement raw material blending process[J]. Chinese Journal of Chemical Engineering, 2019, 27(11): 2734-2741.

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A novel chemical composition estimation model for cement raw material blending process

A novel chemical composition estimation model for cement raw material blending process

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